Machine learning for real-time single-trial EEG-analysis: From brain-computer interfacing to mental state monitoring

Klaus Robert Müller, Michael Tangermann, Guido Dornhege, Matthias Krauledat, Gabriel Curio, Benjamin Blankertz

Research output: Contribution to journalArticlepeer-review

367 Citations (Scopus)

Abstract

Machine learning methods are an excellent choice for compensating the high variability in EEG when analyzing single-trial data in real-time. This paper briefly reviews preprocessing and classification techniques for efficient EEG-based brain-computer interfacing (BCI) and mental state monitoring applications. More specifically, this paper gives an outline of the Berlin brain-computer interface (BBCI), which can be operated with minimal subject training. Also, spelling with the novel BBCI-based Hex-o-Spell text entry system, which gains communication speeds of 6-8 letters per minute, is discussed. Finally the results of a real-time arousal monitoring experiment are presented.

Original languageEnglish
Pages (from-to)82-90
Number of pages9
JournalJournal of Neuroscience Methods
Volume167
Issue number1
DOIs
Publication statusPublished - 2008 Jan 15

Bibliographical note

Funding Information:
We gratefully acknowledge financial support by the Bundesministerium für Bildung und Forschung (BMBF), 01IBE01A/B and 01IGQ0414, by the Deutsche Forschungsgemeinschaft (DFG), FOR 375/B1, and by the IST Programme of the European Community, under the PASCAL Network of Excellence, IST-2002-506778.

Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.

Keywords

  • EEG
  • Machine learning
  • Mental state monitoring
  • Real-time
  • Sensorimotor rhythms
  • Single-trial EEG-analysis
  • α-Rhythm

ASJC Scopus subject areas

  • General Neuroscience

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